调好神经元漏电参数，让脉冲网络在激光雷达避障上媲美传统神经网络。
On the Importance of Neural Membrane Potential Leakage for LIDAR-based Robot Obstacle Avoidance using Spiking Neural Networks
- 通过调节脉冲神经元的膜电位漏电常数，提升SNN对激光雷达数据的处理精度。
- 在自建机器人平台上实现与非脉冲CNN相当的避障控制精度。
- 首次系统研究膜电位漏电对避障性能的影响，适合神经形态计算研究者。
近年来，由于脉冲神经网络（SNN）在神经形态硬件上实现高精度、低内存与低计算复杂度推理的能力，其在机器人应用中备受关注。这使其特别适合电池资源和载荷受限的自主机器人（如无人机、火星车）。本文研究了利用SNN直接从激光雷达（LIDAR）数据进行机器人导航与避障。搭建了配备激光雷达的定制机器人平台，采集带有标注的激光雷达感知数据及人类操作的避障控制指令。关键的是，本文首次系统研究了神经元膜电位泄漏对SNN在处理激光雷达数据时精度的影响。结果表明，通过精细调节脉冲漏积分放（LIF）神经元的膜电位泄漏常数，可在避障任务中达到与非脉冲卷积神经网络（CNN）相当的控制精度。此外，本研究收集的激光雷达数据集已开源，以促进后续研究。
原文摘要 · Abstract (English)
Using neuromorphic computing for robotics applications has gained much attention in recent year due to the remarkable ability of Spiking Neural Networks (SNNs) for high-precision yet low memory and compute complexity inference when implemented in neuromorphic hardware. This ability makes SNNs well-suited for autonomous robot applications (such as in drones and rovers) where battery resources and payload are typically limited. Within this context, this paper studies the use of SNNs for performing direct robot navigation and obstacle avoidance from LIDAR data. A custom robot platform equipped with a LIDAR is set up for collecting a labeled dataset of LIDAR sensing data together with the human-operated robot control commands used for obstacle avoidance. Crucially, this paper provides what is, to the best of our knowledge, a first focused study about the importance of neuron membrane leakage on the SNN precision when processing LIDAR data for obstacle avoidance. It is shown that by carefully tuning the membrane potential leakage constant of the spiking Leaky Integrate-and-Fire (LIF) neurons used within our SNN, it is possible to achieve on-par robot control precision compared to the use of a non-spiking Convolutional Neural Network (CNN). Finally, the LIDAR dataset collected during this work is released as open-source with the hope of benefiting future research.
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